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The causal effects of diabetes disease management in general practice on hospitalizations

Abstract:

Disease management programs (DMPs) in the general practice sector are increasingly used to improve the health of chronically ill patients and reduce hospitalizations and thereby costs. The aim of the present study is to estimate the causal effects of the enrollment of general practices (GP) in a DMP based on electronic health records (EHRs) and information feedback reports on diabetes patients’ total hospitalizations, diabetes-related hospitalizations and hospitalizations with diabetes and cardiovascular-related ambulatory care sensitive conditions (ACSCs). We use a rich nationwide panel dataset (2004-2013) with information on stepwise enrollment of GPs in the EHR program. As a control group, we use GPs who never enrolled. Following the recent literature on causal inference with panel data, we use a standard propensity score matching estimator where we also match on pre-treatment outcomes, which allows controlling for all of the unobservable confounders that were already present in the pre-treatment outcomes. Alternatively, we use a difference in difference and find similar results. Our results show that enrollment in EHR reduced diabetes patients’ risk of hospitalizations by more than 10%. The results are comparable to studies on EHR programs from California, and the magnitudes of the effects are comparable to DMPs that include both EHR and financial incentives.

Speaker:

Giovanni Mellace - University of Southern Denmark

Short bio:

Disease management programs (DMPs) in the general practice sector are increasingly used to improve the health of chronically ill patients and reduce hospitalizations and thereby costs. The aim of the present study is to estimate the causal effects of the enrollment of general practices (GP) in a DMP based on electronic health records (EHRs) and information feedback reports on diabetes patients’ total hospitalizations, diabetes-related hospitalizations and hospitalizations with diabetes and cardiovascular-related ambulatory care sensitive conditions (ACSCs). We use a rich nationwide panel dataset (2004-2013) with information on stepwise enrollment of GPs in the EHR program. As a control group, we use GPs who never enrolled. Following the recent literature on causal inference with panel data, we use a standard propensity score matching estimator where we also match on pre-treatment outcomes, which allows controlling for all of the unobservable confounders that were already present in the pre-treatment outcomes. Alternatively, we use a difference in difference and find similar results. Our results show that enrollment in EHR reduced diabetes patients’ risk of hospitalizations by more than 10%. The results are comparable to studies on EHR programs from California, and the magnitudes of the effects are comparable to DMPs that include both EHR and financial incentives.